Specific Circular RNA Signature of Endothelial Cells: Potential Implications in Vascular Pathophysiology

Circular RNAs (circRNAs) are a recently characterized family of gene transcripts forming a covalently closed loop of single-stranded RNA. The extent of their potential for fine-tuning gene expression is still being discovered. Several studies have implicated certain circular RNAs in pathophysiological processes within vascular endothelial cells and cancer cells independently. However, to date, no comparative study of circular RNA expression in different types of endothelial cells has been performed and analysed through the lens of their central role in vascular physiology and pathology. In this work, we analysed publicly available and original RNA sequencing datasets from arterial, veinous, and lymphatic endothelial cells to identify common and distinct circRNA expression profiles. We identified 4713 distinct circRNAs in the compared endothelial cell types, 95% of which originated from exons. Interestingly, the results show that the expression profile of circular RNAs is much more specific to each cell type than linear RNAs, and therefore appears to be more suitable for distinguishing between them. As a result, we have discovered a specific circRNA signature for each given endothelial cell type. Furthermore, we identified a specific endothelial cell circRNA signature that is composed four circRNAs: circCARD6, circPLXNA2, circCASC15 and circEPHB4. These circular RNAs are produced by genes that are related to endothelial cell migration pathways and cancer progression. More detailed studies of their functions could lead to a better understanding of the mechanisms involved in physiological and pathological (lymph)angiogenesis and might open new ways to tackle tumour spread through the vascular system.

Nuclear HMGB1 protects from nonalcoholic fatty liver disease through negative regulation of liver X receptor

Dysregulations of lipid metabolism in the liver may trigger steatosis progression, leading to potentially severe clinical consequences such as nonalcoholic fatty liver diseases (NAFLDs). Molecular mechanisms underlying liver lipogenesis are very complex and fine-tuned by chromatin dynamics and multiple key transcription factors. Here, we demonstrate that the nuclear factor HMGB1 acts as a strong repressor of liver lipogenesis. Mice with liver-specific Hmgb1 deficiency display exacerbated liver steatosis, while Hmgb1-overexpressing mice exhibited a protection from fatty liver progression when subjected to nutritional stress. Global transcriptome and functional analysis revealed that the deletion of Hmgb1 gene enhances LXRα and PPARγ activity. HMGB1 repression is not mediated through nucleosome landscape reorganization but rather via a preferential DNA occupation in a region carrying genes regulated by LXRα and PPARγ. Together, these findings suggest that hepatocellular HMGB1 protects from liver steatosis development. HMGB1 may constitute a new attractive option to therapeutically target the LXRα-PPARγ axis during NAFLD.

Feature selection for kernel methods in systems biology

The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a ℓ₁ penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/.

Ten simple rules for switching from face-to-face to remote conference: An opportunity to estimate the reduction in GHG emissions

In 2020, the world faced the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic that drastically altered people's lives. Since then, many countries have been forced to suspend public gatherings, leading to many conference cancellations, postponements, or reorganizations. Switching from a face-to-face to a remote conference became inevitable and the ultimate solution to sustain scientific exchanges at the national and the international levels. The same year, as a committee, we were in charge of organizing the major French annual conference that covers all computational biology areas: The "Journées Ouvertes en Biologie, Informatique et Mathématiques" (JOBIM). Despite the health crisis, we succeeded in changing the conference format from face to face to remote in a very short amount of time. Here, we propose 10 simple rules based on this experience to modify a conference format in an optimized and cost-effective way. In addition to the suggested rules, we decided to emphasize an unexpected benefit of this situation: a significant reduction in greenhouse gas (GHG) emissions related to travel for scientific conference attendance. We believe that even once the SARS-CoV-2 crisis is over, we collectively will have an opportunity to think about the way we approach such scientific events over the longer term.

Conserved white-rot enzymatic mechanism for wood decay in the Basidiomycota genus Pycnoporus

White-rot (WR) fungi are pivotal decomposers of dead organic matter in forest ecosystems and typically use a large array of hydrolytic and oxidative enzymes to deconstruct lignocellulose. However, the extent of lignin and cellulose degradation may vary between species and wood type. Here, we combined comparative genomics, transcriptomics and secretome proteomics to identify conserved enzymatic signatures at the onset of wood-decaying activity within the Basidiomycota genus Pycnoporus. We observed a strong conservation in the genome structures and the repertoires of protein-coding genes across the four Pycnoporus species described to date, despite the species having distinct geographic distributions. We further analysed the early response of P. cinnabarinus, P. coccineus and P. sanguineus to diverse (ligno)-cellulosic substrates. We identified a conserved set of enzymes mobilized by the three species for breaking down cellulose, hemicellulose and pectin. The co-occurrence in the exo-proteomes of H₂O₂-producing enzymes with H₂O₂-consuming enzymes was a common feature of the three species, although each enzymatic partner displayed independent transcriptional regulation. Finally, cellobiose dehydrogenase-coding genes were systematically co-regulated with at least one AA9 lytic polysaccharide monooxygenase gene, indicative of enzymatic synergy in vivo. This study highlights a conserved core white-rot fungal enzymatic mechanism behind the wood-decaying process.

Experimental quantification of pollen with DNA metabarcoding using ITS1 and trnL

Although the use of metabarcoding to identify taxa in DNA mixtures is widely approved, its reliability in quantifying taxon abundance is still the subject of debate. In this study we investigated the relationships between the amount of pollen grains in mock solutions and the abundance of high-throughput sequence reads and how the relationship was affected by the pollen counting methodology, the number of PCR cycles, the type of markers and plant species whose pollen grains have different characteristics. We found a significant positive relationship between the number of DNA sequences and the number of pollen grains in the mock solutions. However, better relationships were obtained with light microscopy as a pollen grain counting method compared with flow cytometry, with the chloroplastic trnL marker compared with ribosomal ITS1 and with 30 when compared with 25 or 35 PCR cycles. We provide a list of recommendations to improve pollen quantification.

Unsupervised multiple kernel learning for heterogeneous data integration

Motivation

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account.

Results

We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system.

Availability and implementation

Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

Contact

jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Jflow: a workflow management system for web applications

SUMMARY

Biologists produce large data sets and are in demand of rich and simple web portals in which they can upload and analyze their files. Providing such tools requires to mask the complexity induced by the needed High Performance Computing (HPC) environment. The connection between interface and computing infrastructure is usually specific to each portal. With Jflow, we introduce a Workflow Management System (WMS), composed of jQuery plug-ins which can easily be embedded in any web application and a Python library providing all requested features to setup, run and monitor workflows.

AVAILABILITY AND IMPLEMENTATION

Jflow is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/jflow. The package is coming with full documentation, quick start and a running test portal.

CONTACT

Jerome.Mariette@toulouse.inra.fr.

jvenn: an interactive Venn diagram viewer

Background

Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability.

Results

jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams.

Conclusions

jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn.

Novel avian coronavirus and fulminating disease in guinea fowl, France

For decades, French guinea fowl have been affected by fulminating enteritis of unclear origin. By using metagenomics, we identified a novel avian gammacoronavirus associated with this disease that is distantly related to turkey coronaviruses. Fatal respiratory diseases in humans have recently been caused by coronaviruses of animal origin.

The genome of the white-rot fungus Pycnoporus cinnabarinus: a basidiomycete model with a versatile arsenal for lignocellulosic biomass breakdown

BACKGROUND

Saprophytic filamentous fungi are ubiquitous micro-organisms that play an essential role in photosynthetic carbon recycling. The wood-decayer Pycnoporus cinnabarinus is a model fungus for the study of plant cell wall decomposition and is used for a number of applications in green and white biotechnology.

RESULTS

The 33.6 megabase genome of P. cinnabarinus was sequenced and assembled, and the 10,442 predicted genes were functionally annotated using a phylogenomic procedure. In-depth analyses were carried out for the numerous enzyme families involved in lignocellulosic biomass breakdown, for protein secretion and glycosylation pathways, and for mating type. The P. cinnabarinus genome sequence revealed a consistent repertoire of genes shared with wood-decaying basidiomycetes. P. cinnabarinus is thus fully equipped with the classical families involved in cellulose and hemicellulose degradation, whereas its pectinolytic repertoire appears relatively limited. In addition, P. cinnabarinus possesses a complete versatile enzymatic arsenal for lignin breakdown. We identified several genes encoding members of the three ligninolytic peroxidase types, namely lignin peroxidase, manganese peroxidase and versatile peroxidase. Comparative genome analyses were performed in fungi displaying different nutritional strategies (white-rot and brown-rot modes of decay). P. cinnabarinus presents a typical distribution of all the specific families found in the white-rot life style. Growth profiling of P. cinnabarinus was performed on 35 carbon sources including simple and complex substrates to study substrate utilization and preferences. P. cinnabarinus grew faster on crude plant substrates than on pure, mono- or polysaccharide substrates. Finally, proteomic analyses were conducted from liquid and solid-state fermentation to analyze the composition of the secretomes corresponding to growth on different substrates. The distribution of lignocellulolytic enzymes in the secretomes was strongly dependent on growth conditions, especially for lytic polysaccharide mono-oxygenases.

CONCLUSIONS

With its available genome sequence, P. cinnabarinus is now an outstanding model system for the study of the enzyme machinery involved in the degradation or transformation of lignocellulosic biomass.

RNAbrowse: RNA-Seq de novo assembly results browser

Transcriptome analysis based on a de novo assembly of next generation RNA sequences is now performed routinely in many laboratories. The generated results, including contig sequences, quantification figures, functional annotations and variation discovery outputs are usually bulky and quite diverse. This article presents a user oriented storage and visualisation environment permitting to explore the data in a top-down manner, going from general graphical views to all possible details. The software package is based on biomart, easy to install and populate with local data. The software package is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/RNAbrowse.

Whole-genome, deep pyrosequencing analysis of a duck influenza A virus evolution in swine cells

We studied the sub-population level evolution of a duck influenza A virus isolate during passage in swine tracheal cells. The complete genomes of the A/mallard/Netherlands/10-Nmkt/1999 strain and its swine cell-passaged descendent were analysed by 454 pyrosequencing with coverage depth ranging from several hundred to several thousand reads at any point. This allowed characterization of defined minority sub-populations of gene segments 2, 3, 4, 5, 7, and 8 present in the original isolate. These minority sub-populations ranged between 9.5% (for segment 2) and 46% (for segment 4) of their respective gene segments in the parental stock. They were likely contributed by one or more viruses circulating within the same area, at the same period and in the same or a sympatric host species. The minority sub-populations of segments 3, 4, and 5 became extinct upon viral passage in swine cells, whereas the minority sub-populations of segments 2, 7 and 8 completely replaced their majority counterparts. The swine cell-passaged virus was therefore a three-segment reassortant and also harboured point mutations in segments 3 and 4. The passaged virus was more homogenous than the parental stock, with only 17 minority single nucleotide polymorphisms present above 5% frequency across the whole genome. Though limited here to one sample, this deep sequencing approach highlights the evolutionary versatility of influenza viruses whereby they exploit their genetic diversity, predilection for mixed infection and reassortment to adapt to a new host environmental niche.

Microbial ecology of the rumen evaluated by 454 GS FLX pyrosequencing is affected by starch and oil supplementation of diets

To provide a comprehensive examination of the bacterial diversity in the rumen content of cows fed different diets, high-throughput 16S rRNA gene-based pyrosequencing was used. Four rumen fistulated nonlactating Holstein cows received 12 kg of dry matter per day of four diets based on maize silage during four periods: the low-starch diet (22% starch, 3% fat); the high-starch diet, supplemented with wheat plus barley (35% starch, 3% fat); the low-starch plus oil diet, supplemented with 5% of sunflower oil (20% starch, 7.6% fat) and the high-starch plus oil diet (33% starch, 7.3% fat). Samples were taken after 12 days of adaptation, 5 h postfeeding. Whatever the diet, bacterial community of sieved rumen contents was dominated by Firmicutes and Bacteroidetes. Lachnospiraceae, Ruminococcaceae, Prevotellaceae, and Rikenellaceae families were highly present and were clearly affected by cow diet. The highest abundance of Prevotellaceae and the lowest abundance of Ruminococcaceae and Rikenellaceae were found with the high-starch plus oil diet. Dietary starch increased the relative abundance of only three genera: Barnesiella, Oribacterium and Olsenella, but decreased the relative abundances of several genera, with very significant effects for Rikenellaceae_RC9 and Butyrivibrio-Pseudobutyrivibrio. Oil alone had a limited effect, but interestingly, starch plus oil addition differently affected the bacterial populations compared to starch addition without oil.

NG6: Integrated next generation sequencing storage and processing environment

BACKGROUND

Next generation sequencing platforms are now well implanted in sequencing centres and some laboratories. Upcoming smaller scale machines such as the 454 junior from Roche or the MiSeq from Illumina will increase the number of laboratories hosting a sequencer. In such a context, it is important to provide these teams with an easily manageable environment to store and process the produced reads.

RESULTS

We describe a user-friendly information system able to manage large sets of sequencing data. It includes, on one hand, a workflow environment already containing pipelines adapted to different input formats (sff, fasta, fastq and qseq), different sequencers (Roche 454, Illumina HiSeq) and various analyses (quality control, assembly, alignment, diversity studies,…) and, on the other hand, a secured web site giving access to the results. The connected user will be able to download raw and processed data and browse through the analysis result statistics. The provided workflows can easily be modified or extended and new ones can be added. Ergatis is used as a workflow building, running and monitoring system. The analyses can be run locally or in a cluster environment using Sun Grid Engine.

CONCLUSIONS

NG6 is a complete information system designed to answer the needs of a sequencing platform. It provides a user-friendly interface to process, store and download high-throughput sequencing data.

Involvement of tissue bacteria in the onset of diabetes in humans: evidence for a concept

AIMS/HYPOTHESIS

Evidence suggests that bacterial components in blood could play an early role in events leading to diabetes. To test this hypothesis, we studied the capacity of a broadly specific bacterial marker (16S rDNA) to predict the onset of diabetes and obesity in a general population.

METHODS

Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.) is a longitudinal study with the primary aim of describing the history of the metabolic syndrome. The 16S rDNA concentration was measured in blood at baseline and its relationship with incident diabetes and obesity over 9 years of follow-up was assessed. In addition, in a nested case-control study in which participants later developed diabetes, bacterial phylotypes present in blood were identified by pyrosequencing of the overall 16S rDNA gene content.

RESULTS

We analysed 3,280 participants without diabetes or obesity at baseline. The 16S rDNA concentration was higher in those destined to have diabetes. No difference was observed regarding obesity. However, the 16S rDNA concentration was higher in those who had abdominal adiposity at the end of follow-up. The adjusted OR (95% CIs) for incident diabetes and for abdominal adiposity were 1.35 (1.11, 1.60), p = 0.002 and 1.18 (1.03, 1.34), p = 0.01, respectively. Moreover, pyrosequencing analyses showed that participants destined to have diabetes and the controls shared a core blood microbiota, mostly composed of the Proteobacteria phylum (85-90%).

CONCLUSIONS/INTERPRETATION

16S rDNA was shown to be an independent marker of the risk of diabetes. These findings are evidence for the concept that tissue bacteria are involved in the onset of diabetes in humans.

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).